Statistical image modeling for semantic segmentation

Semantic image segmentation (SIS) is one of the most crucial steps toward image understanding. In this paper, a novel framework to enable SIS is proposed by modeling images automatically. The statistical model for an image is automatically obtained by using a finite mixture model to approximate the underlying class distributions of image pixels. To accurately characterize the principal visual properties of the underlying dominant image compounds, a novel improved Expectation-Maximization (EM) algorithm is presented to select model structure and estimate model parameters simultaneously. Experiments were conducted and convincing results are obtained.